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20162023
most citedAnalytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

74 citations · 200 across the 10 of their papers we have counts for

collaborators

6 papers

cs.CV20221 cited

Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification

Bo Zhang, Jiakang Yuan, Baopu Li +3

Few-shot fine-grained learning aims to classify a query image into one of a set of support categories with fine-grained differences. Although learning different objects' local diff…

cs.LG202274 cited

Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Fan Bao, Chongxuan Li, Jun Zhu +1

Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate…

cs.NE201938 cited

Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search

Xiangxiang Chu, Bo Zhang, Ruijun Xu +1

Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinf…

cs.CV20176 cited

Fast Deep Matting for Portrait Animation on Mobile Phone

Bingke Zhu, Yingying Chen, Jinqiao Wang +3

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction…

cs.CV20162 cited

Max-Margin Deep Generative Models for (Semi-)Supervised Learning

Chongxuan Li, Jun Zhu, Bo Zhang

Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. How…

cs.CV201648 cited

Bootstrapping Face Detection with Hard Negative Examples

Shaohua Wan, Zhijun Chen, Tao Zhang +2

Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks. In this report, a novel approach for training state-of-th…